Fundamental core concepts of risks, processes and solutions for Health Insurers and its partners. I use this same as training material in Industry workshops. Feel free to reach me for any comments, queries or further discussions on the subject

1. Health Insurance
Fundamentals
Dr (Maj) Mukund Kulkarni
Free lancer-Consultant- Insurance, Corporate Wellness & Healthcare
drmukundkulkarni@yahoo.co.in
+91 9833566112
8/5/2019 Dr (Maj) Mukund Kulkarni 1

2. Agenda
• Customer- Insurer Ecosystem
• Products
• Underwriting
• Claims
• FWA
• Data Analytics & Predictive model
• Customer- Insurance journey
• Customer Engagements- Wellness
• Insurance Medicine services
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3. Customer-Insurer Ecosystem
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4. Customer-Insurer Ecosystem
Customer – Beneficiary for the Policy proceeds
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Claim – Legal right as per Insurance contractual terms
Well connected
Well Informed
Wide choices
Need to customize
Ease of Interaction
Low Discrimination (Banks vs Insurance)
Dynamic
Data lakes
Insight pools
Deep dive-Life cycle
Partner vs Payer approach
Understanding Customers
Insurer approach
TRUST
CUSTOMIZE
DIFFERENTIATE
 Business Cycle
 Internal ecosystem
 External ecosystem
INSURER

5. KYC- Customer risk classification
Regulatory
Identity, Residence proof
Customer
DD
Financials
Others
Reporting
Suspicious cases
Suspicious customers
DPlaws
Documentation
Records
Attestation
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Pre-
Membership
UW information
Agent information
Industry databases
Others
Membership
Change requests (SA, Nominee,
Member, etc)
Lapsation/Reinstatement behaviour
Others
Claim
History
Intimation mode
Claimed event
Cooperation and followup
Interactions
Complaint
Cust Satisfaction Outcome
Others
CRI
(Customer Risk Index)KYC Index
CURI
(Customer
UW Risk
Index)
CPSRI
(Customer
Policy
servicing Risk
Index)
CCRI
(Customer
Claims Risk
Index)
MANDATORY CUSTOMISED
Regulatory adherence
Risk customization
Reduce Claims leakages
Customer mgt/Satisfaction
Operational Optimization
Reputation
Benefits
Data and technology
Awareness and training
Cross functional integration
Industry collaboration
Dependencies
Holistic Customer segmentation based on
risk parameters

6. HI- PRODUCTS
AND PRICING
• Types of HI products
• Coverage snapshot
• Development & Risk factors
• Filing and review process
• Pricing concepts and factors, Life vs Health,
Experience analyses

7. HI Products snapshot…
INDEMNITY
In-Patient
R & B
Consul
tant/P
ractiti
oner’s
fee
Diagn
ostics
Pharm
acy
Pre-
Post
H
Others
*
Out-Patient
Dayca
re
Consul
tant
fee
Diagn
ostics
Pharm
acies
Dental
Physio
therap
y
Others
FIXED BENEFIT
HCB SCB OSB DCB CI B DI B Disability
Partial
Perma
nent
Total
Perma
nent
Presu
mptiv
e
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* As per IRDA Health regulations 2016
INDIVIDUAL
GROUP

8. Product development…model
PRODUCT DEPT
• Overall responsibility
• Regulator
coordination/compliance
• Operational guidelines ACTUARIAL
• Pricing
• Experience analyses
• Exception analyses
• Pricing related advise
UNDERWRITING
• Risk selection
• Risk management
CLAIMS
• Contractual liability
adjudication
• Risk management
• Cost containment
• Feedbacks
LEGAL
• Wordings
• Regulator compliance
IT
• Automation
• Proessing
MKTG/SALES
• Feedbacks
• Trainings
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• Filing process
• Withdrawal
• Review
• Group guideline
• Proposal form
• Pricing principle
• Coverage
• Limits
• Age entry/exit
• Exclusions
• Wordings
• UW philosophy
• Eligibility
• Target mkt
• Distribution
• Risk guidelines
KEY FACTORS

9. Control Cycle…
DESIGN-
PRODUCT
PRICE-
ACTUARIAL
SELL-
MARKETING &
SALES
SELECT-
UNDERWRITING
CONCEPTS
MANAGE COSTS-
CLAIMS
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• Mkt research
• Competition analyses
• Sales feedbacks
• Consumer feedbacks
• Regulatory
• Global developments
• Company strategy
• Data mining
• Company strategy
• Risk appetite
• Key Assumptions
• Business objectives
• Experience analysis
• Strategy
• Budget
• Business targets
• Contingency plan
• Trainings
• Deviations (e.g Fraud)
• Distribution
• Sales performance
• UW requirements
• DC quality
• UW guidelines
• UW capabilities
• Medical advancements
• Provider contracting
• Customer experience
• Fraud management
• Cost management
• Protocols
• Medical advancements
• Claims guidelines
• Claims capabilities
• Others

10. Pricing concepts… Premium pricing should be based on “adequacy” and “equity”
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Life Insurance Health Insurance
Pre-defined amount Variable
Single claim Multiple claims
Inflation, Economic changes and
medical advancements don’t affect
Do Affect
Geographic constant Geographic variable
Mortality rates Morbidity rates
NET PREMIUM
(Mortality/Morbidity rates +
Investments + Lapse rate)
LOADING
(Contingency reserve + operating costs)
GROSS PREMIUM
Fundamental principles underlying the pricing of health insurance are the
same as those of Life Insurance
• Block of policies
• Expected Mortality
• Mortality experience
• Classified Mortality charts
• Basic parameters- Age, Gender, Smoking status
Mortality rate
• Interests
• Long term period
• Conservative mortality assumption
• Policy dividends
Investments
• Distribution, Infrastructure, Taxation, Others
• Lapsation effect
Operating Expenses
Net Amount at Risk = Face Amount – Policy reserve

11. UNDERWRITING…
• Introduction & Concept
• Underwriting Process, types and guidelines
• Underwriting Risk classification
• Automation

12. UW concepts…
The process of determining the level of risk presented by the applicant, and
deciding whether to accept the policy, and if so, at what terms and at what price?
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Risk
Classification
Risk Rating
DEFINITION
PURPOSE
• To maintain Equity amongst policyholders
• Protect company from Anti-Selection
• Keep risk within Actuarial pricing assumptions
FUNDAMENTAL RISKS
• Adverse selection:
High risks have increased tendency to buy insurance while low risks have
decreased tendency to buy insurance
• Moral Hazard:
Individuals use more goods and services when their losses are insured
• WP
• Exclusions
• Risk classification
• Risk rating
• Deductibles
• Co-pay
• Sub limits
• Pre-authorization
Risk Mitigants

13. UW process…
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Evidence
Risks assessed
UW Expertise UW Guidelines
Customer
communicationSTP
Simple
• Buss Strategy
• Competition
• Automation
• Med Advancements
• Claims experience
• Others
• Proposal form
• KYC/Financial docs
• Medical reports
• MER
• Fam Physician
• Others

14. UW Risk classification…
PREFERRED
• Better than STD
mortality
expectations
• Reduced
premium
STANDARD
• Avg risk
• STD differs as
per various
parameters
• Unconditional
acceptance
• 94% lives are
STD
RATING
• 16 tables(A-P)
with +25 EMR
ratings
• Multiple
impairments
(Synergistic/Anta
gonistic)
EXCLUSION
• Risk clearly
identifiable
• Legal wording
• Temporary or
Permanent
• Limits on no. of
exclusion
• Medical
exclusions-
complexity at
claims stage
POSTPONE
• Unclear risk
• Risks with
predictable
outcome/change
imminent
• Temporary
period of
deterrence
• Operational
costraints
DECLINE
• Clearly
identifiable high
risks
• Not in sync with
pricing
assumptions
• Permanent in
nature
• Customer
communication
• Decline
database
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15. Automation- Underwriting
15
Complex
High value
cases
Medical cases-
with Medical test
reports
Medical cases- Proposal
disclosures
Non Medical cases
STP
M
A
N
U
A
L
A
U
T
O
UW recommendation
Expert Medical Opinions
UW recommendation
Automation- Non Medical rules
Automation- Non medical rules
1. UW Recommendation
2. Expert Medical Opinions
1. Sub STD-UW recommendation
2. STD- Minor medical disclosures
1. UW Guideline based AI rules
2. Basic non medical rules as per
Pricing/UW guidelines customised.

16. UW OPEX…
• Reward good risks
• Equitable ratings
• Limited re-pricing
• Avoid Claims op costs
• Minimize Representation
• Enhance Customer sat
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17. HRA- Health Risk assessment
‘A systematic approach to collecting information from individuals that identifies risk factors, provides individualized
feedback, and links the person with at least one intervention to promote health, sustain function and/or prevent disease’
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GENERAL
SOCIAL
LIFESTYLE
PSYCHOLO
GICAL
MEDICAL
• Demography
• Geographic risks
• Financial status
• Others
• Interactions
• Participations
• Memberships
• Others
• Fitness initiatives
• Behavioural- Smoking/Alcohol/Drugs
• Nutrition and Dietetics
• Others
• Perception/Insight
• Confidence
• Motivation
• Readiness to change
• Spiritual
• Others
• Biometrics
• Health history
• Inheritance
• Examination findings
• Lab reports
• Others
HRA highlights health risks but does not diagnose disease and should not replace consultation with a medical practitioner
HRA
Identification- Risks
Prediction- Morbidity/Mortality
Measurement- DALY/Productivity
Evaluation- Efficacy of Wellness Programs
Org
Team
Indl
Levels Purpose
CVS predictor
Oncology
Dementia
Diabetes
Others
Specific Calculators

18. CLAIMS
MANAGEMENT
• Case Adjudication
• Investigations
• Decisioning
• Sec 45 Insurance Act
• Claims Risk management
• Regulatory aspects

19. CLAIMS ADJUDICATION... GENERAL
IDENTITY
Documents
Physical check
Data based
POLICY STATUS
Inforce
Limit
exhaustion
Lapse
Terminated
Others
CONFIRMATION OF
CLAIMED EVENT
Diagnosis &
Treatment
Provider
parameters
Protocol
adherence
Treatment
process
TERMS AND
CONDITIONS
Exclusions
Waiting period
Limits
PRE-EXISTING
CONDITION
Investigation
findings
Sec 45
applicability
Misrepresentat
ion/Non
disclosure
Legal Opinion
PAYMENT
Accounting
NEFT
Confrimation
Discharge and
feedback
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Re-imbursementCashless

20. PAY
DENY
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21. Non disclosure handling…
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22. Exclusions & Waiting periods
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23. Claims Maturity model
Set Reference benchmark
Holistic assessment
Identify gaps & Leakages
Identify scope of improvements
Competitive comparison
Management information
Others
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Exercise to evaluate and score Claims and associated functions on their respective
relative maturity to derive a baseline for current and future reference.
Departmental
•Overall benchmark
•Goal
•Management
information
Transactional
•Indl parameters
•Objectives
•Operational
information
Purpose
Types
Reduce leakages/Losses
Best practices
Customer satisfaction
New Business opportunities
Regulatory
Reputation, brand
Competitive advantages
Benefits
Independent assessment
Holistic assessment
Dedicated approach
Map current competencies
Futuristic approach
Key Statements

24. Claims Maturity model- Dept level
Customer
handling
Claims Registration
Follow-up
Payment process
Regulatory Adherence
SLA
Customer Feedbacks
Grievance redressal
Analytics
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Triage
Investigations
Matrix
Authorities
Verification parameters
Manual/Auto assignment
Fraud triggers
Outcome analyses
Claims
Adjudication
Authority
Case assignment
Expert opinions
Fraud triggers
Documentation
Regulatory adherence
Technology
Claims system
Integration
Data capture
Analytic capability
Reporting capability
Innovations- ML
Qualityreview Frequency
Prospective/Retrospective
Audit team expertise
Data/Automation
Outcome
Analyses
Others
People
Training
Provider management
Business Intelligence
RCU
Innovations e.g
Drone/Sensors
• Legacy
• Mainstream
• Leading

25. Claims Maturity model- Transactional level
Walkin Postal Tele Email Online Mobile app
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Intimation
Documents Manual Scanning Indexing DMS OCR ML
Manual Basic system Customised System generated DynamicTriggers
Assignment Manual Basic System Auto-assign Investigator mapped Monitoring
Competency level Training level Authority matrix Non tech Tech ExpertsPeople
System No systems Legacy System validation Automation DSS
No guidelines Basic (Non-Tech) Advanced (Tech) Dynamic ML basedGuidelines
Single level decision Peer review DSS based Technical inputs (EMO) OthersQuality
Manual Electronic SLA Integration Discharge liabilityPayment
SLA STD Communication Dedicated resource
Customized
communication
C Sat surveyCustomer
Registration
Investigation
Assessment
Customer
Mapping current stage of each step in Claims transaction on the scale of Process Improvement scale (based on
market, regulatory and individual best practices) will allow find suggestions to improve for any Insurer.

26. 1. No policy of life insurance shall be called in question on any ground whatsoever after the expiry of three years from the date of the
policy, i.e., from the date of issuance of the policy or the date of commencement of risk or the date of revival of the policy or the date of
the rider to the policy, whichever is later.
2. A policy of life insurance may be called in question at any time within three years from the date of issuance of the policy or the date of
commencement of risk or the date of revival of the policy or the date of the rider to the policy, whichever is later, on the ground of fraud:
Provided that the insurer shall have to communicate in writing to the insured or the legal representatives or nominees or assignees of
the insured the grounds and materials on which such decision is based.
• Explanation I.—For the purposes of this sub-section, the expression "fraud" means any of the following acts committed by the insured or by his agent,
with intent to deceive the insurer or to induce the insurer to issue a life insurance policy:— (a) the suggestion, as a fact of that which is not true and
which the insured does not believe to be true; (b) the active concealment of a fact by the insured having knowledge or belief of the fact; (c) any other
act fitted to deceive; and (d) any such act or omission as the law specially declares to be fraudulent.
• Explanation II.—Mere silence as to facts likely to affect the assessment of the risk by the insurer is not fraud, unless the circumstances of the case are
such that regard being had to them, it is the duty of the insured or his agent keeping silence, to speak, or unless his silence is, in itself, equivalent to
speak.
3. Notwithstanding anything contained in sub-section (2), no insurer shall repudiate a life insurance policy on the ground of fraud if the
insured can prove that the misstatement of or suppression of a material fact was true to the best of his knowledge and belief or that
there was no deliberate intention to suppress the fact or that such misstatement of or suppression of a material fact are within the
knowledge of the insurer: Provided that in case of fraud, the onus of disproving lies upon the beneficiaries, in case the policyholder is not
alive.
• Explanation.—A person who solicits and negotiates a contract of insurance shall be deemed for the purpose of the formation of the contract, to be the
agent of the insurer.
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Section 45- Insurance act 1938…………………(1/2)

27. Section 45- Insurance act 1938…………………(2/2)
4. A policy of life insurance may be called in question at any time within three years from the date of issuance of the policy
or the date of commencement of risk or the date of revival of the policy or the date of the rider to the policy, whichever is
later, on the ground that any statement of or suppression of a fact material to the expectancy of the life of the insured was
incorrectly made in the proposal or other document on the basis of which the policy was issued or revived or rider issued:
Provided that the insurer shall have to communicate in writing to the insured or the legal representatives or nominees or
assignees of the insured the grounds and materials on which such decision to repudiate the policy of life insurance is based:
Provided further that in case of repudiation of the policy on the ground of misstatement or suppression of a material fact,
and not on the ground of fraud, the premiums collected on the policy till the date of repudiation shall be paid to the insured
or the legal representatives or nominees or assignees of the insured within a period of ninety days from the date of such
repudiation.
• Explanation.—For the purposes of this sub-section, the misstatement of or suppression of fact shall not be considered material
unless it has a direct bearing on the risk undertaken by the insurer, the onus is on the insurer to show that had the insurer been
aware of the said fact no life insurance policy would have been issued to the insured.
5. Nothing in this section shall prevent the insurer from calling for proof of age at any time if he is entitled to do so, and no
policy shall be deemed to be called in question merely because the terms of the policy are adjusted on subsequent proof that
the age of the life insured was incorrectly stated in the proposal.'.
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28. Claims risk management
Prospective
Cashless
Pre-
Authori
zation
Procure
ment
Utilizati
on
review
Dischar
ge
plannin
g
Reimbursement
Medical
protoco
ls
Claims
guidelin
es
IRDA
defined
non
payable
items
Retrospective
Portfolio analytics
Experie
nce
analysis
Predicti
ve
analytic
s
Excepti
ons
Quality reviews
(Audits)
Technic
al
review
Leakage
rates
Process
review
Operational
Provider contracting
Due
diligenc
e
Tariff
negotiat
ion
Quality
review
Tariff
negotiations
Market
inputs
Trend
monitor
ing
Medical
protocols
Contrac
ting
Reviews
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29. Customer Claims journey
• Care coordination- Concept
• Trigger and data points- OPEX
• Customization- Health Risk assessment(HRA)
• Claims Decision and Impact
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30. Customer Journey
Pre-sales/Sales UW/NB Policy Servicing Claim
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Profile
Insurance
need
Purpose
of buying
Lead
generatio
n
Distributi
on choice
Existing
Insurance
Others
Risks Social Financial
Occupatio
nal
Behaviour
al
Type of
product
Risk
coverage
applied
Other
Insurance
Others
Member
change
Residence
change
Coverage
change
Nomination
change
Porting Others
Event
Circumsta
nces
Contribut
ors
Complicat
ion
Outcome
Claim
frequency
Interactio
n triggers
Provider
selection
Beneficiar
y details
Others
Industry data
Market reports
Others
Agent report
Proposal form
UW Questionnaire
Social media
Others
POS Data
Social media
Industry data
Others
Claims history
Event information
Investigation report
Provider reports
Claim form
Others
CUSTOMER SEGMENTATION RISK SELECTION FRAUD TRIGGERS RISK MGT (DISEASE/CASE MGT) PROGRAMS C-SATOPEX QUALITY
TRIGGERS
DATA
SOURCE
OUTCOME
Identifying triggers/Opportunities by mapping Customer journey at all stages to benefit the Insurer in both aspects-
Internally (Risk management/OPEX) and Externally (Customer management)

31. Claims Decision- Impact
Decision Category Customer reaction Reasons Recommendation
PAID Contractual Fraud customers are happy Insurance is a need and not a
choice
• Automate
• Capture more experience and triggers
Ex-Gratia Satisfied Out of the way support • Management approval
• Accounting
• Monitor
DENIED T & C Generally not complaining Clear evidence • Review coverage constraints to improve
Exclusions Dissatisfied - Fine print
- UW exclusions not
communicated
- Others
• Review Sales process
• Review UW process
• Review customer communication
ND/PED Only Fraud customers are
quiet
- Contractual challenge
- Misunderstanding of
clauses
- Fraudulent behaviour
• Expert Opinions mandatory
• Policy voidance
• Legal preparation
• Industry wide action
• Case study
PENDING • Documents
• Information
• Legal aspects
Dissatisfied - Contractual wordings
- Fine print
- Non STD Documentation
• Review wordings
• Review process
• Standardize process
• Communication
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Fraudulent Innocent
Material
Non-
Material
ND

32. FRAUD IN HI
(CONCEPT)
• Definition and types, triggers
• Fraud analyses- Audit (Leakage rates), Data
analytics
• Fraud management and actions

33. FWA (Fraud/Waste and Abuse)…
• 6% of Global healthcare spending lost in Frauds(GDP of Malaysia)
• Loss of INR 6 Billion every year (10-15% claims)
• Increase costs for Insurer by 1-5%
• Avg ticket size 25-75k INR
• Lack of “Anti-Fraud framework”/ Anti-Fraud department
• India- 9% losses (Forensic report 2012)
• UK- 2.1; France- 3.9; US- 80, SA- 15 Bi USD losses
( Clyde and Co Global Fraud report 2016)
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FRAUD (IRDA)- "an act or omission intended to gain dishonest or unlawful
advantage for a party committing the fraud or for other related parties ."
ABUSE- “Practices that are inconsistent with business ethics or medical
practices and result in an unnecessary cost to claims”
DEFINITIONS
MAGNITUDE
ASSOCIATIONS

34. Types
Customer
(Policyholder)
Intermediary
(Broker/Agent)
Internal staff
(Insurer)
Service
provider
(Hospitals)
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• Non disclosures/Misrepresentations
• Document manipulation
• Impersonation
• Insurance shopping
• Staged/Feigned event
• Data fudging (E.g. Group covers)
• Kick-backs
• Favours
• Collusion
• Premium siphoning
• Medical necessity
• Overcharging
• False charging
• Un-bundling
• Up-coding
• Over-utilization
• Document manipulation

35. Claims Audit…leakage rates
• Audit of claim files
• Definitions---
• Sampling
• Levels of leakages
• Weightage
• Dashboard
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Hard leakage Soft leakage
Process
improvements
Commercial
decision
Underpayment
•Unconfirmed identity
•Pre-auth process error
Claim
Registration
•Validity
•Exclusions
•WP
Coverage
•Medical necessity
•Co-payment
•Calculation error
•Expert referral
Adjudication
•Tariff non adherence
•LOS
•Insufficient challenge
Provider
Line of business
Value band
Customer profile
Distribution
Provider wise
Diagnosis
Treatment
Contractual vs Commercial

36. 7 Step Data mining method
Applying supervised methods as a routine online processing task and applying unsupervised methods (outlier detection and clustering) in specific time periods for refining the
previous steps and detecting new cases of fraud.
Designing supervised models based on labelled records of previous step and selecting the most discriminative features (Lieu et al., 2008)
Identifying outlier cluster (s) and investigating records in those clusters in more detail and determining fraudulent or abusive records (e.g. by inspection) (Lin et al., 2008)
Excluding outliers from the data and clustering (or re-clustering) records based on extracted features (Lin et al., 2008)
Identifying unusual records by outlier detection methods for detailed investigation (Shan et al., 2009)
Defining new features that are indicators of fraudulent or abusive behaviour by expert domains or automated algorithms such as association rules induction (Li et al., 2008; Shan et
al., 2008)
Identifying the most important attributes of data by expert domains (Sokol et al., 2001; Li et al., 2008)
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https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4796421/

37. FRAUD MANAGEMENT
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INSURER
Tele-Underwriting
Promoting Pre-Authorization
Claim document standardization
Benefit explanation
Name-Shame guidelines
Technology to counter fraud
Whistle-blower policy
Anti-Fraud & Risk committee
INDUSTRY
Councils (E.g. GIC, AIC)
Common data pool
Medical protocols
Provider accreditation and
registration
Education- Industry awareness
Contracting- Model contracts
Collaborative actions
Watchlist of providers
Fraud Investigator training
program
Whistle-blower/reward system
Police training
Anti-Fraud bureau (e.g. NHCAA,
CAIF)
REGULATOR/GOVERNMENT
Process guidelines
Regular reviews/Audits
Revoke/Suspend licence
Specific laws against fraud
Forfeit/ Claw back provision
Anti Fraud public messaging
Dedicated Anti-Fraud department
Fraud risk assessment framework
Council
Common database
Regulations/Guidelines/Specific
laws

38. Data Analytics & Predictive
models
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39. Scope of analytics in Life Insurance…
Customer relations
• Acquisition
• Retention
• Interactions
• Satisfaction
• Others Risk management
• Actuarial
• Underwriting
• Policy servicing
• Fraud, Waste and
Abuse
• Claims
• Others
Operational
• Productivity
• TAT
• Quality
• Efficiency
• Others
Business
• Benchmarking
• Products
• Others
Vendor management
• Operational
• Legal
• Value addition
• CBA
• Others
• Data
• Data tools
• Resources
• Strategy
• Budget
• Regulations/Legal
• Others
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• AI
• ML
• IOT
• Blockchain

40. Customer Classification- life time value (LTV)…
Demographics
• Age
• Gender
• Marital Status
• Credit score
• Relationship
• Insurance density
• Social data
• Others
Product
• Type & Features
• Premium/face amount
• Tenure/Age of policy
• Sales channel
• Buying behavior
• Other policies
• Others
Transaction details
• Lapsation/Reinstatement behavior
• Touch points
• Change requests
• Claims history (In LB policies)
• Value added programs
• CSAT survey
• Others
Non engagement details
• Medical/Health events
• Social data
• Change in life style
• Others
Pre-Acquisition Post-Acquisition
Sales Underwriting Policy servicing Persistency Claims
CURRENT VALUE FUTURE VALUE
Customer acquisition UW risk index (CURI) Cust Pol Servicing risk index (CPSRI) Cust Claims indexScoring
model
Predictive Analytics
SILVER GOLD PLATINUMCustomer
LTV
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41. Predictive
Model…
Training/Evaluation datasets
Data
Transformati
on
Key Variables
Data
preparation
Review
Deployment
Evaluation
Integration
Modelling
High
Medium
Low
Output
Scores
Focus/Promote/Prefer
Low focus/effort
Deny/Trigger/Alarm
Operational
suggestion
DS 1 DS 2 DS 3 DS 4 Others
Data Sources
Internal
External
Third party
Others
Guidelines
Policies
Regulations
Others
Data
Independent
factors
Predictive modeling can be defined as the
analysis of large data sets to make inferences
or identify meaningful relationships, and the
use of these relationships to better predict
future events
Information Business rules
• Feature Engineering
• Categorical values
• Missing values
• Outlier mgt
• Others
• Variable generation
• Explorative data
analyses
Variable selections, Data index factors
and their respective weightages need
to be adjusted in a dynamic manner
8/5/2019 Dr (Maj) Mukund Kulkarni 41

42. Predictive
Model…
Underwriting
Training/Evaluation datasets
Data
Transformati
on
Key Variables
Data
preparation
Review
Deployment
Evaluation
Integration
Modelling
High
Medium
Low
CURI
Accept- STP (Triaging)
Accept with conditions
Denial
Operational
suggestion
Demography Credit info Medical info Past history Social Others
Data Sources
Internal
External
Third party
Others
UW Guidelines
Medical guidelines
Product guidelines
Others
Data
Independent
factors
Information Business rules
• Feature Engineering
• Categorize Age/Income, etc.
• Missing values
• Outlier mgt
• Others
• Traditional vs Non
traditional
• Dependent vs
Independent
• Explorative data
analyses
• Health risk
calculators e.g CVS
• Triaging
• Cost reduction
• Efficiency
• Standardization
• CSat
Benefits
8/5/2019 Dr (Maj) Mukund Kulkarni 42

43. Predictive
Model…
Customer Acquisition
Training/Evaluation datasets
Data
Transformati
on
Key Variables
Data
preparation
Review
Deployment
Evaluation
Integration
Modelling
High
Medium
Low
Propensity &
Potential scores
Engagement strategy
Product Strategy
Monitoring segments
Operational
suggestion
Psychography Survey Shopping info Social Financial Others
Data Sources
Internal
External
Third party
Others
Industry databases
Product specific weightage
Time specific weightages
E.g Q4
Others
Data
Independent
factors
Information Business rules
• Feature Engineering
• Categorical variables
• Missing values
• Outlier mgt
• Others
• Traditional vs Non
traditional
• Dependent vs
Independent
• Explorative data
analyses
• Front line
• Quality of risk
• Resource mgt
• Efficiency
• Csat
Benefits
8/5/2019 Dr (Maj) Mukund Kulkarni 43

44. Predictive
Model…
FWA
Training/Evaluation datasets
Data
Transformati
on
Key Variables
Data
preparation
Review
Deployment
Evaluation
Integration
Modelling
High
Medium
Low
Fraud propensity
score
Denial/Legal action/Reporting
Close Watchlist
Low focus
Operational
suggestion
Claims data UW/POS data
Industry Fraud
list
Provider data Others
Data Sources
Internal
External
Third party
Others
Fraud trigger list
Anti fraud guidelines
Industry reported factors
Others
Data
Independent
factors
Information Business rules
• Feature Engineering
• Categorical variables
• Missing values
• Outlier mgt
• Others
• Traditional vs Non
traditional
• Dependent vs
Independent
• Explorative data
analyses
• Savings
• Reputation
• Cost reduction
• Csat
• Efficiency
Benefits
8/5/2019 Dr (Maj) Mukund Kulkarni 44

45. Other Opportunities…
• Customer Retention
• Customer Segmentation – Marketing
• Customer Segmentation- Wellness interventions
• Distribution analytics- Agency mgt
• Claims prediction
• Inforce management
• Medical Underwriting- risk prediction
• Provider grading and recommendation- Healthcare
• Cause of loss (e.g Death) predictors
• New products & Actuarials
8/5/2019 Dr (Maj) Mukund Kulkarni 45

46. BIG DATA
Automation
Role of AI
• Introduction, Data cycle
• Analytic domain
• Role of AI in Insurance

47. BIG DATA/ AUTOMATION…
05-08-2019 Dr (Maj) Mukund Kulkarni 47
Capture
• Customer info
• Transaction info
• Others
Automate
• Data capture
• Underwriting
• Claims
• Contracting
• Repositories
• POS
Analyse
• Retrospective
• Predictive
Apply
• Risk guidelines
• Traditional
process
• Risk management
process
(Disease/Case
management
programs)
• Others
Customer satisfaction
Customer interaction
High need for Insurers to be more flexible, approachable and closer to
customer behaviour, needs and expectations

48. BIG DATA/ANALYTICS… cotd
SALES
• Cross sell
• Customer segmentation
(Predictive)
• Communication UNDERWRITING
• Predictive
• Accurate pricing
• Faster TAT
• Operating cost(Automate)
• Customised approach
CLAIMS
• Streamline
Investigation(Predictive)
• Claim complexity index
• Fraud detection
• Operating cost(Automate)
PERSISTENCY
• Lapse prediction
• Communication
CUSTOMER ENGAGEMENT
• Case/Disease management
• Value additions
• Customised products, process, advise
OTHERS
05-08-2019 Dr (Maj) Mukund Kulkarni 48

49. AI and Insurance…
05-08-2019 Dr (Maj) Mukund Kulkarni 49
Growth top line (New products/Customers/Geographies)
Advisory services: Consistent, un-biased, evidence based, low costs
OP Efficiency: Low TAT, Low costs, High productivity
Customer experience: Customised products/solutions, reminders
Competitive advantage: Predict market forces and forecast optimal responses
https://www.cognizant.com/whitepapers/how-insurers-can-harness-artificial-intelligence-codex2131.pdf
BENEFITS

50. INSURANCE
MEDICINE
SERVICES
8/5/2019
Dr (Maj) Mukund Kulkarni 50

51. 8/5/2019 Dr (Maj) Mukund Kulkarni 51

52. 05-08-2019 Dr (Maj) Mukund Kulkarni 52

53. Customer Engagements
Disease/Case management programs
• The Implementation model
• An Illustration
8/5/2019 Dr (Maj) Mukund Kulkarni 53

54. Disease management programs- Model
8/5/2019 Dr (Maj) Mukund Kulkarni 54
Structure/Governance
Market stats
Guidelines
Team Constitution
Budgeting
Governance
Roles and
Responsibilities
Review framework
Assessment
Portfolio
assessment
HRA
Disease load
Evaluation
parameters
Engagement
intensity
Dependencies
Goals/Objectives
Qualitative/Quanti
tative reference
baseline
Alignment with
existing
strategies/process
es
Selection and
classification of
WP
Outcome
measurements
Project plan with
timeline spread
(ST/MT/LT) ProgramStrategy
Mapping Vendor
programs
CBA- Self/Vendor
driven
Member
engagement plan
Member
incentivization
Implementationplan
Targets
Timelines and
calenderization
Plans Execution
Record and data
maintenance
(wellness
calendar)
Review&Evaluation
Program Outcome
review
Recommendation
Review guidelines
Outcome
parameters review
Feedbacks and
Surveys

55. Disease management programs-
Illustration (Obesity)
8/5/2019 Dr (Maj) Mukund Kulkarni 55
Structure/Governance
Market stats
(Obesity stats)
Guidelines (E.g
AACE guidelines)
Team Constitution
(Medical, Health,
HR, PMO
members)
Budgeting (cater
for fitness events,
trackers, data
tools, training
sessions etc.)
Governance
Roles and
Responsibilities
Review framework
Assessment Portfolio
assessment
(High BMI people,
Claims costs,
Complications,
etc.)
HRA
(Overall and
specific HRA)
Disease load
(claims costs)
Evaluation
parameters
(BMI, Lipid levels)
Engagement
intensity
Dependencies
Goals/Objectives
Qualitative/Quanti
tative reference
baseline
(Avg BMI)
Alignment with
existing
strategies/process
es
Selection and
classification of
WP
(Fitness events,
trackers,
Consultation, trg
sessions)
Outcome
measurements
(BMI, Lipid levels)
Project plan with
timeline spread
(ST/MT/LT)
ProgramStrategy
Mapping Vendor
programs
CBA- Self/Vendor
driven
Member
engagement plan
(Calendarization)
Member
incentivization
(Gym vouchers,
food vouchers,
premium redn)
Implementationplan
Targets
(BMI reduction
2%)
Timelines and
calenderization
Plans Execution
Record and data
maintenance
(wellness
calendar)
Review&Evaluation
Program Outcome
review
(Avg BMI, Lipid
levels,
Complications)
Recommendation
Review guidelines
Outcome
parameters review
(claims costs,
IP/OP, etc.)
Feedbacks and
Surveys

56. Thank you….
Dr (Maj) Mukund Kulkarni
Free lancer-Consultant- Insurance, Corporate
Wellness & Healthcare
drmukundkulkarni@yahoo.co.in
+91 9833566112
8/5/2019 Dr (Maj) Mukund Kulkarni 56